Insurance Paper — Pretrained Weights

Pretrained checkpoints for "The Unawareness of AI Looking for Health Insurance Type from Normal Chest X-ray Images".

All models were trained on MIMIC-CXR-JPG v2.0.0 with seed=123. MedGemma experiments use the MedGemma-refined subset (mimic-cxr-gemma), containing only normal CXRs filtered by MedGemma.

Models

Model	Architecture	Params	File size
MedMamba	VSSM_DoubleLinear / VSSM_Double_addDemothen2	~29M	~115 MB
DenseNet121	DenseNetWithDoubleLinear / _addDemothen2	~8M	~59 MB
Swin Transformer V2	SwinTDoubleLinear / _addDemothen2	~50M	~218 MB

Repository Structure

insurance_paper_weights/
├── exp0/                          # Baselines (CheXpert / MIMIC)
│   ├── CheXpert/
│   │   └── densenet.pt, mamba.pt, swinTF.pt
│   └── MIMIC/
│       └── densenet.pt, mamba.pt, swinTF.pt
│
├── exp0-2/                        # Random Initialization
│   └── swinTF_random.pt
│
├── exp1/                          # Patch keep/remove — DenseNet
│   ├── densenet_keep/
│   │   └── patch1.pt ... patch9.pt
│   └── densenet_remove/
│       └── patch1.pt ... patch9.pt
│
├── exp1-1/                        # Patch keep/remove — MedMamba
│   ├── mamba_keep/
│   │   └── patch1.pt ... patch9.pt
│   └── mamba_remove/
│       └── patch1.pt ... patch9.pt
│
├── exp1-2/                        # Patch keep/remove — DenseNet + Swin Transformer
│   ├── densenet_keep/
│   │   └── patch1.pt ... patch9.pt
│   ├── densenet_remove/
│   │   └── patch1.pt ... patch9.pt
│   ├── swinTF_keep/
│   │   └── patch1.pt ... patch9.pt
│   └── swinTF_remove/
│       └── patch1.pt ... patch9.pt
│
├── exp2/                          # Resolution — MedMamba
│   └── mamba_2.pt ... mamba_224.pt (8 files)
│
├── exp2-1/                        # Resolution — DenseNet
│   └── densenet_2.pt ... densenet_224.pt (8 files)
│
├── exp2-2/                        # Resolution — Swin Transformer
│   └── swinTF_4.pt ... swinTF_224.pt (7 files)
│
├── exp3/                          # Demographics — MedMamba
│   ├── sex.pt, age.pt, race.pt
│   ├── sexage.pt, sexrace.pt, agerace.pt
│   └── sexagerace.pt
│
├── exp3-1/                        # Demographics — DenseNet
│   └── (same 7 files)
│
├── exp3-2/                        # Demographics — Swin Transformer
│   └── (same 7 files)
│
├── exp4/                          # Frequency filtering — MedMamba
│   ├── highpass/
│   │   └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
│   └── lowpass/
│       └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
│
├── exp4-1/                        # Frequency filtering — Swin Transformer
│   ├── highpass/ └── lowpass/
│
├── exp4-2/                        # Frequency filtering — DenseNet
│   ├── highpass/ └── lowpass/
│
├── exp2_medgemma/                 # MedGemma Resolution — MedMamba
│   └── mamba_2.pt ... mamba_224.pt (8 files)
│
├── exp2-1_medgemma/               # MedGemma Resolution — DenseNet
│   └── densenet_2.pt ... densenet_224.pt (8 files)
│
├── exp2-2_medgemma/               # MedGemma Resolution — Swin Transformer
│   └── swinTF_4.pt ... swinTF_224.pt (7 files)
│
├── exp3_medgemma/                 # MedGemma Demographics — MedMamba
│   ├── sex.pt, age.pt, race.pt
│   ├── sexage.pt, sexrace.pt, agerace.pt
│   └── sexagerace.pt
│
├── exp3-1_medgemma/               # MedGemma Demographics — DenseNet
│   └── (same 7 files)
│
├── exp3-2_medgemma/               # MedGemma Demographics — Swin Transformer
│   └── (same 7 files)
│
├── exp4_medgemma/                 # MedGemma Frequency filtering — MedMamba
│   ├── highpass/
│   │   └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
│   └── lowpass/
│       └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
│
├── exp4-1_medgemma/               # MedGemma Frequency filtering — Swin Transformer
│   ├── highpass/ └── lowpass/
│
└── exp4-2_medgemma/               # MedGemma Frequency filtering — DenseNet
    ├── highpass/ └── lowpass/

Checkpoint Table

Note: All experiments are trained and available.

Experiment	Description	Model	Config	# Checkpoints	HF path	Available?
exp0	Baselines (CheXpert/MIMIC)	All 3	—	6	`exp0/{dataset}/{model}.pt`	Yes
exp0-2	Random Initialisation	SwinT	—	1	`exp0-2/swinTF_random.pt`	Yes
exp1	Patch keep/remove	DenseNet	9 patches x 2	18	`exp1/densenet_{keep/remove}/patch{N}.pt`	Yes
exp1-1	Patch keep/remove	MedMamba	9 patches x 2	18	`exp1-1/mamba_{keep/remove}/patch{N}.pt`	Yes
exp1-2	Patch keep/remove	SwinT	9 patches x 2	18	`exp1-2/swinTF_{keep/remove}/patch{N}.pt`	Yes
exp2	Resolution	MedMamba	8 resolutions	8	`exp2/mamba_{N}.pt`	Yes
exp2-1	Resolution	DenseNet	8 resolutions	8	`exp2-1/densenet_{N}.pt`	Yes
exp2-2	Resolution	SwinT	7 resolutions	7	`exp2-2/swinTF_{N}.pt`	Yes
exp3	Demographics (addDemo)	MedMamba	7 demo combos	7	`exp3/{combo}.pt`	Yes
exp3-1	Demographics (addDemo)	DenseNet	7 demo combos	7	`exp3-1/{combo}.pt`	Yes
exp3-2	Demographics (addDemo)	SwinT	7 demo combos	7	`exp3-2/{combo}.pt`	Yes
exp4	Freq filtering (HP+LP)	MedMamba	8 freq x 2	16	`exp4/{highpass,lowpass}/{F}Hz.pt`	Yes
exp4-1	Freq filtering (HP+LP)	SwinT	8 freq x 2	16	`exp4-1/{highpass,lowpass}/{F}Hz.pt`	Yes
exp4-2	Freq filtering (HP+LP)	DenseNet	8 freq x 2	16	`exp4-2/{highpass,lowpass}/{F}Hz.pt`	Yes

Original: 153 checkpoints

MedGemma Experiments (mimic-cxr-gemma dataset)

Experiment	Description	Model	Config	# Checkpoints	HF path	Available?
exp2	Resolution	MedMamba	8 resolutions	8	`exp2_medgemma/mamba_{N}.pt`	Yes
exp2-1	Resolution	DenseNet	8 resolutions	8	`exp2-1_medgemma/densenet_{N}.pt`	Yes
exp2-2	Resolution	SwinT	7 resolutions	7	`exp2-2_medgemma/swinTF_{N}.pt`	Yes
exp3	Demographics (addDemo)	MedMamba	7 demo combos	7	`exp3_medgemma/{combo}.pt`	Yes
exp3-1	Demographics (addDemo)	DenseNet	7 demo combos	7	`exp3-1_medgemma/{combo}.pt`	Yes
exp3-2	Demographics (addDemo)	SwinT	7 demo combos	7	`exp3-2_medgemma/{combo}.pt`	Yes
exp4	Freq filtering (HP+LP)	MedMamba	8 freq x 2	16	`exp4_medgemma/{highpass,lowpass}/{F}Hz.pt`	Yes
exp4-1	Freq filtering (HP+LP)	SwinT	8 freq x 2	16	`exp4-1_medgemma/{highpass,lowpass}/{F}Hz.pt`	Yes
exp4-2	Freq filtering (HP+LP)	DenseNet	8 freq x 2	16	`exp4-2_medgemma/{highpass,lowpass}/{F}Hz.pt`	Yes

MedGemma: 92 checkpoints

Total: 153 original + 92 MedGemma = 245 unique checkpoints

Demo combos (exp3): sex, age, race, sexage, sexrace, agerace, sexagerace
Frequencies (exp4): 1, 5, 10, 25, 50, 100, 200, 400 Hz
Resolutions (exp2): 2, 4, 7, 14, 28, 56, 112, 224
Patches (exp1): 1-9, corresponding to a 3x3 grid on 448x448 images (left-to-right, top-to-bottom)

Usage

Download all weights

git lfs install
git clone https://huggingface.co/InsurancePrediction/insurance_paper_weights

Download a single experiment

# Using huggingface_hub
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="InsurancePrediction/insurance_paper_weights",
    allow_patterns="exp3-1/*",
    local_dir="./weights"
)

Load a checkpoint

import torch

# Base model (exp1, exp4)
from model import DenseNetWithDoubleLinear
model = DenseNetWithDoubleLinear(num_classes=2, dropout_prob=0)
ckpt = torch.load("exp1-2/densenet_keep/patch1.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"])

# Demographics model (exp3)
from MedMamba.MedMamba import VSSM_Double_addDemothen2
model = VSSM_Double_addDemothen2(num_classes=2, demo_size=5)  # e.g. sexage -> 2+3=5
ckpt = torch.load("exp3/sexage.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"])

Mapping from HF paths to original training paths

Weights in JAMA_codes were trained on ORCD. Weights marked with (*) were trained by Chi-Yu and uploaded directly — no JAMA_codes equivalent.

HF path	Original training path
`exp0/{dataset}/{model}.pt`	Trained by Chi-Yu
`exp0-2/swinTF_random.pt`	Trained by Chi-Yu
`exp1-1/mamba_keep/patch{N}.pt`	Trained by Chi-Yu
`exp1-1/mamba_remove/patch{N}.pt`	`JAMA_codes/exp1-1/Rand123/Rand123_patchidx{N}_exp1-1_model_aucbest.pt`
`exp1-2/densenet_keep/patch{N}.pt`	`JAMA_codes/densenet_keep/Rand123/Rand123_patchidx{N}_densenet_keep_model_aucbest.pt`
`exp1-2/densenet_remove/patch{N}.pt`	Trained by Chi-Yu
`exp1-2/swinTF_keep/patch{N}.pt`	`JAMA_codes/swinTF_keep/Rand123/Rand123_patchidx{N}_swinTF_keep_model_aucbest.pt`
`exp1-2/swinTF_remove/patch{N}.pt`	`JAMA_codes/swinTF_remove/Rand123/Rand123_patchidx{N}_swinTF_remove_model_aucbest.pt`
`exp2/mamba_{N}.pt`	Trained by Chi-Yu
`exp2-1/densenet_{N}.pt`	Trained by Chi-Yu
`exp2-2/swinTF_{N}.pt`	Trained by Chi-Yu
`exp3/{combo}.pt`	`JAMA_codes/{combo}_weights/mamba/sunday/Rand123/Rand123_{combo}_mamba_sunday_model_aucbest.pt`
`exp3-1/{combo}.pt`	`JAMA_codes/{combo}_weights/densenet/sunday/Rand123/Rand123_{combo}_densenet_sunday_model_aucbest.pt`
`exp3-2/{combo}.pt`	`JAMA_codes/{combo}_weights/swin/sunday/Rand123/Rand123_{combo}_swin_sunday_model_aucbest.pt`
`exp4/{highpass,lowpass}/{F}Hz.pt`	`JAMA_codes/{F}{HighPass,LowPass}_weights/mamba/{direction}/Rand123/...`
`exp4-1/{highpass,lowpass}/{F}Hz.pt`	`JAMA_codes/{F}{HighPass,LowPass}_swin_freq/Rand123/...`
`exp4-2/{highpass,lowpass}/{F}Hz.pt`	`JAMA_codes/{F}{HighPass,LowPass}_densenet_freq/Rand123/...`
`exp2_medgemma/mamba_{N}.pt`	`JAMA_codes_medgemma/{N}_mg_exp2/mamba/Rand123/...`
`exp2-1_medgemma/densenet_{N}.pt`	`JAMA_codes_medgemma/{N}_mg_exp2/densenet/Rand123/...`
`exp2-2_medgemma/swinTF_{N}.pt`	`JAMA_codes_medgemma/{N}_mg_exp2/swinTF/Rand123/...`
`exp3_medgemma/{combo}.pt`	`JAMA_codes_medgemma/{combo}_mg_exp3/mamba/Rand123/...`
`exp3-1_medgemma/{combo}.pt`	`JAMA_codes_medgemma/{combo}_mg_exp3-1/densenet/Rand123/...`
`exp3-2_medgemma/{combo}.pt`	`JAMA_codes_medgemma/{combo}_mg_exp3-2/swinTF/Rand123/...`
`exp4_medgemma/{highpass,lowpass}/{F}Hz.pt`	`JAMA_codes_medgemma/{F}{HighPass,LowPass}_mg_exp4/mamba/Rand123/...`
`exp4-1_medgemma/{highpass,lowpass}/{F}Hz.pt`	`JAMA_codes_medgemma/{F}{HighPass,LowPass}_mg_exp4-1/swinTF/Rand123/...`
`exp4-2_medgemma/{highpass,lowpass}/{F}Hz.pt`	`JAMA_codes_medgemma/{F}{HighPass,LowPass}_mg_exp4-2/densenet/Rand123/...`

Bootstrap Evaluation Status

All 153 original bootstrap evaluations complete (n_bootstrap=20, sample_size=1000, seed=42).

Experiment	Evaluations	Status
exp0	6 (3 models x 2 datasets)	Done
exp0-2	1	Done
exp1-1	18 (mamba keep + remove x 9 patches)	Done
exp1-2	36 (densenet keep/remove + swinTF keep/remove x 9 patches)	Done
exp2/2-1/2-2	23 (8+8+7 resolutions)	Done
exp3/3-1/3-2	21 (3 models x 7 combos)	Done
exp4/4-1/4-2	48 (3 models x 2 directions x 8 freqs)	Done

MedGemma Bootstrap Status

Experiment	Evaluations	Status
MedGemma exp2/2-1/2-2	23 (8+8+7 resolutions)	Done
MedGemma exp3/3-1/3-2	21 (3 models x 7 combos)	Done
MedGemma exp4/4-1/4-2	48 (3 models x 2 directions x 8 freqs)	Done

Results: bootstrap_results/ in the code repository.

Training Details

Dataset (original): MIMIC-CXR-JPG v2.0.0 (normal frontal chest X-rays)
Dataset (MedGemma): mimic-cxr-gemma (MedGemma-refined, normal CXRs only)
Task: Binary classification — Private vs. Public/Government insurance
Image size: 448 x 448
Seed: 123
Selection: Best validation AUC checkpoint

Citation

@inproceedings{chen2025unawareness,
  title={The Unawareness of AI Looking for Health Insurance Type from Normal Chest X-ray Images},
  author={Chen, Chi-Yu and others},
  year={2025}
}

Code

Code Repository

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